Production CI/CD patterns for multi-agent AI systems. Covers LangSmith evaluation pipelines, prompt versioning with S3, canary deployments, regression testing, cost monitoring gates, and automated rollback strategies.
Implementation-ready guide to building the Brain agent in a multi-agent system. Covers LangGraph supervisor patterns, Command API routing, context injection, prompt engineering, state schema design, and cost-optimized model selection.
Production scaling patterns for multi-agent AI systems. Covers AWS Step Functions orchestration, Lambda auto-scaling, Bedrock provisioned throughput, cost optimization strategies, and operational runbooks.
Production security patterns for multi-agent AI systems. Covers IAM least privilege per agent, prompt injection defense, input sanitization, output validation, audit logging, secrets management, and compliance requirements.
Production implementation of state management for multi-agent systems. Covers DynamoDB checkpointing with LangGraph, S3 for large payloads, Athena query patterns, conversation memory, and state recovery strategies.
Complete implementation guide for worker agents in a multi-agent system. Covers tool design patterns, prompt engineering for cheap models, input/output contracts, error handling, and cost optimization with Haiku and DeepSeek.
Production-tested architecture for multi-agent AI systems using LangGraph on AWS. Brain/Worker cost optimization, Step Functions orchestration, DynamoDB checkpointing, CI/CD pipelines, and real-world cost modeling that achieves 5-10x savings.
I experimented with CrewAI and LangGraph to build multi-agent workflows. Some were genuinely useful, some were expensive toys. Here's what I learned.
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